Lu Wei, Xia Lingnan, Tan Tien Ping, Ma Hua
Henan High-Speed Railway Operation and Maintenance Engineering Research Center, Zhengzhou Railway Vocational and Technical College, Zhengzhou, Henan, China.
School of Computer Sciences, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia.
PeerJ Comput Sci. 2024 Dec 23;10:e2610. doi: 10.7717/peerj-cs.2610. eCollection 2024.
Emotion recognition is a significant research problem in affective computing as it has a lot of potential areas of application. One of the approaches in emotion recognition uses electroencephalogram (EEG) signals to identify the emotion of a person. However, effectively using the global and local features of EEG signals to improve the performance of emotion recognition is still a challenge. In this study, we propose a novel Convolution Interactive Transformer Network for EEG Emotion Recognition, known as CIT-EmotionNet, which efficiently integrates the global and local features of EEG signals. We convert the raw EEG signals into spatial-spectral representations, which serve as the inputs into the model. The model integrates convolutional neural network (CNN) and Transformer within a single framework in a parallel manner. We propose a Convolution Interactive Transformer module, which facilitates the interaction and fusion of local and global features extracted by CNN and Transformer respectively, thereby improving the average accuracy of emotion recognition. The proposed CIT-EmotionNet outperforms state-of-the-art methods, achieving an average recognition accuracy of 98.57% and 92.09% on two publicly available datasets, SEED and SEED-IV, respectively.
情感识别是情感计算中的一个重要研究问题，因为它有许多潜在的应用领域。情感识别的方法之一是使用脑电图（EEG）信号来识别一个人的情绪。然而，有效地利用EEG信号的全局和局部特征来提高情感识别的性能仍然是一个挑战。在本研究中，我们提出了一种用于EEG情感识别的新型卷积交互式变压器网络，称为CIT-EmotionNet，它有效地整合了EEG信号的全局和局部特征。我们将原始EEG信号转换为空间频谱表示，作为模型的输入。该模型在单个框架内以并行方式集成了卷积神经网络（CNN）和变压器。我们提出了一个卷积交互式变压器模块，它促进了分别由CNN和变压器提取的局部和全局特征的交互和融合，从而提高了情感识别的平均准确率。所提出的CIT-EmotionNet优于现有方法，在两个公开可用的数据集SEED和SEED-IV上分别实现了98.57%和92.09%的平均识别准确率。
